code: def forward(self, x):
      h1 = self.layers(x.reshape(-1, np.prod(self.input_dim)))
      h21, h22 = self.diag(h1), self.lower(h1)
  
      L = torch.zeros(
          (x.shape[0], self.latent_dim, self.latent_dim)
      ).to(x.device)
      indices = torch.tril_indices(
          row=self.latent_dim, col=self.latent_dim, offset=-1
      )
  
      # get non-diagonal coefficients
      L[:, indices[0], indices[1]] = h22
  
      # add diagonal coefficients
      L = L + torch.diag_embed(h21.exp())
  
      output = ModelOutput(L=L)
  
      return output
  
imports: from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.models.nn import BaseDecoder, BaseDiscriminator, BaseEncoder, BaseMetric
  from ..base.base_utils import ModelOutput
label_desc: Use image node_id 2 for calling the Metric_MLP obj's underlying member method code with example usage: obj.forward(x) and returns: output